Dear all,
I have a question about using categorical predictors for SVM, using "svm"
from library(e1071). If I have multiple categorical predictors, should they
just be included as factors? Take a simple artificial data example:
x1<-rnorm(500)
x2<-rnorm(500)
#Categorical Predictor 1, with 5 levels
x3<-as.factor(rep(c(1,2,3,4,5)
,c(50,150,130,70,100)))
#Catgegorical Predictor 2, with 3 levels
x4<-as.factor(rep(c("R","B","G"),c(100,200,200)))
#Response
y<-rep(c(-1,1),c(275,225))
class<-as.factor(y)
svmdata<-cbind(class,x1,x2,x3,x4)
mod1<-svm(class~.,data=svmdata,type="C-classification")
OR
should each factor be coded as an indicator variable? E.g. for categorical
predictor 2, since there're 3 levels, we code:
(R,R,B,G,G) = ( (1,0,0),(1,0,0),(0,1,0),(0,0,1),(0,0,1) )
There are no errors when I run the model using either method, but I'm unsure
which is correct for svm in 'e1071'.
Many thanks.
V.V.
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